io

Bases: object

Methods

__init__(fname, mode='r', compression=0)

Advanced storage system for tractography based on HDF5

Parameters

fnamestr, full filename

mode‘r’ read

‘w’ write ‘r+’ read and write only if file already exists

compression0 no compression to 9 maximum compression

Examples

>>> import os
>>> from tempfile import mkstemp #temp file
>>> from dipy.io.dpy import Dpy
>>> def dpy_example():
...     fd,fname = mkstemp()
...     fname += '.dpy'#add correct extension
...     dpw = Dpy(fname,'w')
...     A=np.ones((5,3))
...     B=2*A.copy()
...     C=3*A.copy()
...     dpw.write_track(A)
...     dpw.write_track(B)
...     dpw.write_track(C)
...     dpw.close()
...     dpr = Dpy(fname,'r')
...     dpr.read_track()
...     dpr.read_track()
...     dpr.read_tracksi([0, 1, 2, 0, 0, 2])
...     dpr.close()
...     os.remove(fname) #delete file from disk
>>> dpy_example()

close()

read_track()

read one track each time

read_tracks()

read the entire tractography

read_tracksi(indices)

read tracks with specific indices

version()

write_track(track)

write on track each time

write_tracks(tracks)

write many tracks together

Bases: object

Methods

__init__(fname, mode='r', compression=0)

Advanced storage system for tractography based on HDF5

Parameters

fnamestr, full filename

mode‘r’ read

‘w’ write ‘r+’ read and write only if file already exists

compression0 no compression to 9 maximum compression

Examples

>>> import os
>>> from tempfile import mkstemp #temp file
>>> from dipy.io.dpy import Dpy
>>> def dpy_example():
...     fd,fname = mkstemp()
...     fname += '.dpy'#add correct extension
...     dpw = Dpy(fname,'w')
...     A=np.ones((5,3))
...     B=2*A.copy()
...     C=3*A.copy()
...     dpw.write_track(A)
...     dpw.write_track(B)
...     dpw.write_track(C)
...     dpw.close()
...     dpr = Dpy(fname,'r')
...     dpr.read_track()
...     dpr.read_track()
...     dpr.read_tracksi([0, 1, 2, 0, 0, 2])
...     dpr.close()
...     os.remove(fname) #delete file from disk
>>> dpy_example()

close()

read_track()

read one track each time

read_tracks()

read the entire tractography

read_tracksi(indices)

read tracks with specific indices

version()

write_track(track)

write on track each time

write_tracks(tracks)

write many tracks together

Bases: nibabel.tmpdirs.TemporaryDirectory

Create, return, and change directory to a temporary directory

Examples

>>> import os
>>> my_cwd = os.getcwd()
>>> with InTemporaryDirectory() as tmpdir:
...     _ = open('test.txt', 'wt').write('some text')
...     assert os.path.isfile('test.txt')
...     assert os.path.isfile(os.path.join(tmpdir, 'test.txt'))
>>> os.path.exists(tmpdir)
False
>>> os.getcwd() == my_cwd
True

Methods

__init__(suffix='', prefix='tmp', dir=None)

Initialize self. See help(type(self)) for accurate signature.

Bases: dipy.reconst.eudx_direction_getter.EuDXDirectionGetter

Attributes

ang_thr

qa_thr

total_weight

Methods

__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Bases: object

Points on the unit sphere.

The sphere can be constructed using one of three conventions:

Sphere(x, y, z)
Sphere(xyz=xyz)
Sphere(theta=theta, phi=phi)

Parameters

x, y, z1-D array_like

Vertices as x-y-z coordinates.

theta, phi1-D array_like

Vertices as spherical coordinates. Theta and phi are the inclination and azimuth angles respectively.

xyz(N, 3) ndarray

Vertices as x-y-z coordinates.

faces(N, 3) ndarray

Indices into vertices that form triangular faces. If unspecified, the faces are computed using a Delaunay triangulation.

edges(N, 2) ndarray

Edges between vertices. If unspecified, the edges are derived from the faces.

Attributes

x

y

z

Methods

__init__(x=None, y=None, z=None, theta=None, phi=None, xyz=None, faces=None, edges=None)

Initialize self. See help(type(self)) for accurate signature.

edges()

faces()

find_closest(xyz)

Find the index of the vertex in the Sphere closest to the input vector

Parameters

xyzarray-like, 3 elements

A unit vector

Returns

idxint

The index into the Sphere.vertices array that gives the closest vertex (in angle).

subdivide(n=1)

Subdivides each face of the sphere into four new faces.

New vertices are created at a, b, and c. Then each face [x, y, z] is divided into faces [x, a, c], [y, a, b], [z, b, c], and [a, b, c].

      y
      /\
     /  \
   a/____\b
   /\    /\
  /  \  /  \
 /____\/____\
x      c     z

Parameters

nint, optional

The number of subdivisions to preform.

Returns

new_sphereSphere

The subdivided sphere.

vertices()

property x

property y

property z

Bases: enum.Enum

Enum to simplify future change to convention

__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

NIFTI = 'center'

TRACKVIS = 'corner'

Bases: nibabel.streamlines.tractogram.SliceableDataDict

Dictionary for which key access can do slicing on the values.

This container behaves like a standard dictionary but extends key access to allow keys for key access to be indices slicing into the contained ndarray values. The elements must also be ndarrays.

In addition, it makes sure the amount of data contained in those ndarrays matches the number of streamlines given at the instantiation of this instance.

Parameters

n_rowsNone or int, optional

Number of rows per value in each key, value pair or None for not specified.

*args :

**kwargs :

Positional and keyword arguments, passed straight through the dict constructor.

Methods

__init__(n_rows=0, *args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

extend(other)

Appends the elements of another PerArrayDict.

That is, for each entry in this dictionary, we append the elements coming from the other dictionary at the corresponding entry.

Parameters

otherPerArrayDict object

Its data will be appended to the data of this dictionary.

Returns

None

Notes

The keys in both dictionaries must be the same.

Bases: nibabel.streamlines.tractogram.PerArrayDict

Dictionary for which key access can do slicing on the values.

This container behaves like a standard dictionary but extends key access to allow keys for key access to be indices slicing into the contained ndarray values. The elements must also be ArraySequence.

In addition, it makes sure the amount of data contained in those array sequences matches the number of elements given at the instantiation of the instance.

Methods

__init__(n_rows=0, *args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Bases: enum.Enum

Enum to simplify future change to convention

__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

RASMM = 'rasmm'

VOX = 'vox'

VOXMM = 'voxmm'

Bases: object

Class for stateful representation of collections of streamlines Object designed to be identical no matter the file format (trk, tck, vtk, fib, dpy). Facilitate transformation between space and data manipulation for each streamline / point.

Attributes

affine

Getter for the reference affine

data_per_point

Getter for data_per_point

data_per_streamline

Getter for data_per_streamline

dimensions

Getter for the reference dimensions

origin

Getter for origin standard

space

Getter for the current space

space_attributes

Getter for spatial attribute

streamlines

Partially safe getter for streamlines

voxel_order

Getter for the reference voxel order

voxel_sizes

Getter for the reference voxel sizes

Methods

__init__(streamlines, reference, space, origin=<Origin.NIFTI: 'center'>, data_per_point=None, data_per_streamline=None)

Create a strict, state-aware, robust tractogram

Parameters

streamlineslist or ArraySequence

Streamlines of the tractogram

referenceNifti or Trk filename, Nifti1Image or TrkFile,

Nifti1Header, trk.header (dict) or another Stateful Tractogram Reference that provides the spatial attributes. Typically a nifti-related object from the native diffusion used for streamlines generation

spaceEnum (dipy.io.stateful_tractogram.Space)

Current space in which the streamlines are (vox, voxmm or rasmm) After tracking the space is VOX, after loading with nibabel the space is RASMM

originEnum (dipy.io.stateful_tractogram.Origin), optional

Current origin in which the streamlines are (center or corner) After loading with nibabel the origin is CENTER

data_per_pointdict, optional

Dictionary in which each key has X items, each items has Y_i items X being the number of streamlines Y_i being the number of points on streamlines #i

data_per_streamlinedict, optional

Dictionary in which each key has X items X being the number of streamlines

Notes

Very important to respect the convention, verify that streamlines match the reference and are effectively in the right space.

Any change to the number of streamlines, data_per_point or data_per_streamline requires particular verification.

In a case of manipulation not allowed by this object, use Nibabel directly and be careful.

property affine

Getter for the reference affine

static are_compatible(sft_1, sft_2)

Compatibility verification of two StatefulTractogram to ensure space, origin, data_per_point and data_per_streamline consistency

compute_bounding_box()

Compute the bounding box of the streamlines in their current state

Returns

outputndarray

8 corners of the XYZ aligned box, all zeros if no streamlines

property data_per_point

Getter for data_per_point

property data_per_streamline

Getter for data_per_streamline

property dimensions

Getter for the reference dimensions

static from_sft(streamlines, sft, data_per_point=None, data_per_streamline=None)

Create an instance of StatefulTractogram from another instance of StatefulTractogram.

Parameters

streamlineslist or ArraySequence

Streamlines of the tractogram

sftStatefulTractgram,

The other StatefulTractgram to copy the space_attribute AND state from.

data_per_pointdict, optional

Dictionary in which each key has X items, each items has Y_i items X being the number of streamlines Y_i being the number of points on streamlines #i

data_per_streamlinedict, optional

Dictionary in which each key has X items X being the number of streamlines

—–

get_data_per_point_keys()

Return a list of the data_per_point attribute names

get_data_per_streamline_keys()

Return a list of the data_per_streamline attribute names

get_streamlines_copy()

Safe getter for streamlines (for slicing)

is_bbox_in_vox_valid()

Verify that the bounding box is valid in voxel space. Negative coordinates or coordinates above the volume dimensions are considered invalid in voxel space.

Returns

outputbool

Are the streamlines within the volume of the associated reference

property origin

Getter for origin standard

remove_invalid_streamlines(epsilon=0.001)

Remove streamlines with invalid coordinates from the object. Will also remove the data_per_point and data_per_streamline. Invalid coordinates are any X,Y,Z values above the reference dimensions or below zero

Parameters

epsilonfloat (optional)

Epsilon value for the bounding box verification. Default is 1e-6.

Returns

outputtuple

Tuple of two list, indices_to_remove, indices_to_keep

property space

Getter for the current space

property space_attributes

Getter for spatial attribute

property streamlines

Partially safe getter for streamlines

to_center()

Safe function to shift streamlines so the center of voxel is the origin

to_corner()

Safe function to shift streamlines so the corner of voxel is the origin

to_origin(target_origin)

Safe function to change streamlines to a particular origin standard False means NIFTI (center) and True means TrackVis (corner)

to_rasmm()

Safe function to transform streamlines and update state

to_space(target_space)

Safe function to transform streamlines to a particular space using an enum and update state

to_vox()

Safe function to transform streamlines and update state

to_voxmm()

Safe function to transform streamlines and update state

property voxel_order

Getter for the reference voxel order

property voxel_sizes

Getter for the reference voxel sizes

Bases: object

Container for streamlines and their data information.

Streamlines of a tractogram can be in any coordinate system of your choice as long as you provide the correct affine_to_rasmm matrix, at construction time. When applied to streamlines coordinates, that transformation matrix should bring the streamlines back to world space (RAS+ and mm space) 1.

Moreover, when streamlines are mapped back to voxel space 2, a streamline point located at an integer coordinate (i,j,k) is considered to be at the center of the corresponding voxel. This is in contrast with other conventions where it might have referred to a corner.

References

1

http://nipy.org/nibabel/coordinate_systems.html#naming-reference-spaces

2

http://nipy.org/nibabel/coordinate_systems.html#voxel-coordinates-are-in-voxel-space

Attributes

streamlinesArraySequence object

Sequence of \(T\) streamlines. Each streamline is an ndarray of shape (\(N_t\), 3) where \(N_t\) is the number of points of streamline \(t\).

data_per_streamlinePerArrayDict object

Dictionary where the items are (str, 2D array). Each key represents a piece of information \(i\) to be kept alongside every streamline, and its associated value is a 2D array of shape (\(T\), \(P_i\)) where \(T\) is the number of streamlines and \(P_i\) is the number of values to store for that particular piece of information \(i\).

data_per_pointPerArraySequenceDict object

Dictionary where the items are (str, ArraySequence). Each key represents a piece of information \(i\) to be kept alongside every point of every streamline, and its associated value is an iterable of ndarrays of shape (\(N_t\), \(M_i\)) where \(N_t\) is the number of points for a particular streamline \(t\) and \(M_i\) is the number values to store for that particular piece of information \(i\).

Methods

__init__(streamlines=None, data_per_streamline=None, data_per_point=None, affine_to_rasmm=None)

Parameters

streamlinesiterable of ndarrays or ArraySequence, optional

Sequence of \(T\) streamlines. Each streamline is an ndarray of shape (\(N_t\), 3) where \(N_t\) is the number of points of streamline \(t\).

data_per_streamlinedict of iterable of ndarrays, optional

Dictionary where the items are (str, iterable). Each key represents an information \(i\) to be kept alongside every streamline, and its associated value is an iterable of ndarrays of shape (\(P_i\),) where \(P_i\) is the number of scalar values to store for that particular information \(i\).

data_per_pointdict of iterable of ndarrays, optional

Dictionary where the items are (str, iterable). Each key represents an information \(i\) to be kept alongside every point of every streamline, and its associated value is an iterable of ndarrays of shape (\(N_t\), \(M_i\)) where \(N_t\) is the number of points for a particular streamline \(t\) and \(M_i\) is the number scalar values to store for that particular information \(i\).

affine_to_rasmmndarray of shape (4, 4) or None, optional

Transformation matrix that brings the streamlines contained in this tractogram to RAS+ and mm space where coordinate (0,0,0) refers to the center of the voxel. By default, the streamlines are in an unknown space, i.e. affine_to_rasmm is None.

property affine_to_rasmm

Affine bringing streamlines in this tractogram to RAS+mm.

apply_affine(affine, lazy=False)

Applies an affine transformation on the points of each streamline.

If lazy is not specified, this is performed in-place.

Parameters

affinendarray of shape (4, 4)

Transformation that will be applied to every streamline.

lazy{False, True}, optional

If True, streamlines are not transformed in-place and a LazyTractogram object is returned. Otherwise, streamlines are modified in-place.

Returns

tractogramTractogram or LazyTractogram object

Tractogram where the streamlines have been transformed according to the given affine transformation. If the lazy option is true, it returns a LazyTractogram object, otherwise it returns a reference to this Tractogram object with updated streamlines.

copy()

Returns a copy of this Tractogram object.

property data_per_point

property data_per_streamline

extend(other)

Appends the data of another Tractogram.

Data that will be appended includes the streamlines and the content of both dictionaries data_per_streamline and data_per_point.

Parameters

otherTractogram object

Its data will be appended to the data of this tractogram.

Returns

None

Notes

The entries in both dictionaries self.data_per_streamline and self.data_per_point must match respectively those contained in the other tractogram.

property streamlines

to_world(lazy=False)

Brings the streamlines to world space (i.e. RAS+ and mm).

If lazy is not specified, this is performed in-place.

Parameters

lazy{False, True}, optional

If True, streamlines are not transformed in-place and a LazyTractogram object is returned. Otherwise, streamlines are modified in-place.

Returns

tractogramTractogram or LazyTractogram object

Tractogram where the streamlines have been sent to world space. If the lazy option is true, it returns a LazyTractogram object, otherwise it returns a reference to this Tractogram object with updated streamlines.

Bases: object

product(*iterables, repeat=1) –> product object

Cartesian product of input iterables. Equivalent to nested for-loops.

For example, product(A, B) returns the same as: ((x,y) for x in A for y in B). The leftmost iterators are in the outermost for-loop, so the output tuples cycle in a manner similar to an odometer (with the rightmost element changing on every iteration).

To compute the product of an iterable with itself, specify the number of repetitions with the optional repeat keyword argument. For example, product(A, repeat=4) means the same as product(A, A, A, A).

product(‘ab’, range(3)) –> (‘a’,0) (‘a’,1) (‘a’,2) (‘b’,0) (‘b’,1) (‘b’,2) product((0,1), (0,1), (0,1)) –> (0,0,0) (0,0,1) (0,1,0) (0,1,1) (1,0,0) …

__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Bases: object

Methods

__init__(fname, mode='r', compression=0)

Advanced storage system for tractography based on HDF5

Parameters

fnamestr, full filename

mode‘r’ read

‘w’ write ‘r+’ read and write only if file already exists

compression0 no compression to 9 maximum compression

Examples

>>> import os
>>> from tempfile import mkstemp #temp file
>>> from dipy.io.dpy import Dpy
>>> def dpy_example():
...     fd,fname = mkstemp()
...     fname += '.dpy'#add correct extension
...     dpw = Dpy(fname,'w')
...     A=np.ones((5,3))
...     B=2*A.copy()
...     C=3*A.copy()
...     dpw.write_track(A)
...     dpw.write_track(B)
...     dpw.write_track(C)
...     dpw.close()
...     dpr = Dpy(fname,'r')
...     dpr.read_track()
...     dpr.read_track()
...     dpr.read_tracksi([0, 1, 2, 0, 0, 2])
...     dpr.close()
...     os.remove(fname) #delete file from disk
>>> dpy_example()

close()

read_track()

read one track each time

read_tracks()

read the entire tractography

read_tracksi(indices)

read tracks with specific indices

version()

write_track(track)

write on track each time

write_tracks(tracks)

write many tracks together

Bases: enum.Enum

Enum to simplify future change to convention

__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

NIFTI = 'center'

TRACKVIS = 'corner'

Bases: enum.Enum

Enum to simplify future change to convention

__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

RASMM = 'rasmm'

VOX = 'vox'

VOXMM = 'voxmm'

Bases: object

Class for stateful representation of collections of streamlines Object designed to be identical no matter the file format (trk, tck, vtk, fib, dpy). Facilitate transformation between space and data manipulation for each streamline / point.

Attributes

affine

Getter for the reference affine

data_per_point

Getter for data_per_point

data_per_streamline

Getter for data_per_streamline

dimensions

Getter for the reference dimensions

origin

Getter for origin standard

space

Getter for the current space

space_attributes

Getter for spatial attribute

streamlines

Partially safe getter for streamlines

voxel_order

Getter for the reference voxel order

voxel_sizes

Getter for the reference voxel sizes

Methods

__init__(streamlines, reference, space, origin=<Origin.NIFTI: 'center'>, data_per_point=None, data_per_streamline=None)

Create a strict, state-aware, robust tractogram

Parameters

streamlineslist or ArraySequence

Streamlines of the tractogram

referenceNifti or Trk filename, Nifti1Image or TrkFile,

Nifti1Header, trk.header (dict) or another Stateful Tractogram Reference that provides the spatial attributes. Typically a nifti-related object from the native diffusion used for streamlines generation

spaceEnum (dipy.io.stateful_tractogram.Space)

Current space in which the streamlines are (vox, voxmm or rasmm) After tracking the space is VOX, after loading with nibabel the space is RASMM

originEnum (dipy.io.stateful_tractogram.Origin), optional

Current origin in which the streamlines are (center or corner) After loading with nibabel the origin is CENTER

data_per_pointdict, optional

Dictionary in which each key has X items, each items has Y_i items X being the number of streamlines Y_i being the number of points on streamlines #i

data_per_streamlinedict, optional

Dictionary in which each key has X items X being the number of streamlines

Notes

Very important to respect the convention, verify that streamlines match the reference and are effectively in the right space.

Any change to the number of streamlines, data_per_point or data_per_streamline requires particular verification.

In a case of manipulation not allowed by this object, use Nibabel directly and be careful.

property affine

Getter for the reference affine

static are_compatible(sft_1, sft_2)

Compatibility verification of two StatefulTractogram to ensure space, origin, data_per_point and data_per_streamline consistency

compute_bounding_box()

Compute the bounding box of the streamlines in their current state

Returns

outputndarray

8 corners of the XYZ aligned box, all zeros if no streamlines

property data_per_point

Getter for data_per_point

property data_per_streamline

Getter for data_per_streamline

property dimensions

Getter for the reference dimensions

static from_sft(streamlines, sft, data_per_point=None, data_per_streamline=None)

Create an instance of StatefulTractogram from another instance of StatefulTractogram.

Parameters

streamlineslist or ArraySequence

Streamlines of the tractogram

sftStatefulTractgram,

The other StatefulTractgram to copy the space_attribute AND state from.

data_per_pointdict, optional

Dictionary in which each key has X items, each items has Y_i items X being the number of streamlines Y_i being the number of points on streamlines #i

data_per_streamlinedict, optional

Dictionary in which each key has X items X being the number of streamlines

—–

get_data_per_point_keys()

Return a list of the data_per_point attribute names

get_data_per_streamline_keys()

Return a list of the data_per_streamline attribute names

get_streamlines_copy()

Safe getter for streamlines (for slicing)

is_bbox_in_vox_valid()

Verify that the bounding box is valid in voxel space. Negative coordinates or coordinates above the volume dimensions are considered invalid in voxel space.

Returns

outputbool

Are the streamlines within the volume of the associated reference

property origin

Getter for origin standard

remove_invalid_streamlines(epsilon=0.001)

Remove streamlines with invalid coordinates from the object. Will also remove the data_per_point and data_per_streamline. Invalid coordinates are any X,Y,Z values above the reference dimensions or below zero

Parameters

epsilonfloat (optional)

Epsilon value for the bounding box verification. Default is 1e-6.

Returns

outputtuple

Tuple of two list, indices_to_remove, indices_to_keep

property space

Getter for the current space

property space_attributes

Getter for spatial attribute

property streamlines

Partially safe getter for streamlines

to_center()

Safe function to shift streamlines so the center of voxel is the origin

to_corner()

Safe function to shift streamlines so the corner of voxel is the origin

to_origin(target_origin)

Safe function to change streamlines to a particular origin standard False means NIFTI (center) and True means TrackVis (corner)

to_rasmm()

Safe function to transform streamlines and update state

to_space(target_space)

Safe function to transform streamlines to a particular space using an enum and update state

to_vox()

Safe function to transform streamlines and update state

to_voxmm()

Safe function to transform streamlines and update state

property voxel_order

Getter for the reference voxel order

property voxel_sizes

Getter for the reference voxel sizes

Bases: object

Container for streamlines and their data information.

Streamlines of a tractogram can be in any coordinate system of your choice as long as you provide the correct affine_to_rasmm matrix, at construction time. When applied to streamlines coordinates, that transformation matrix should bring the streamlines back to world space (RAS+ and mm space) 3.

Moreover, when streamlines are mapped back to voxel space 4, a streamline point located at an integer coordinate (i,j,k) is considered to be at the center of the corresponding voxel. This is in contrast with other conventions where it might have referred to a corner.

References

3

http://nipy.org/nibabel/coordinate_systems.html#naming-reference-spaces

4

http://nipy.org/nibabel/coordinate_systems.html#voxel-coordinates-are-in-voxel-space

Attributes

streamlinesArraySequence object

Sequence of \(T\) streamlines. Each streamline is an ndarray of shape (\(N_t\), 3) where \(N_t\) is the number of points of streamline \(t\).

data_per_streamlinePerArrayDict object

Dictionary where the items are (str, 2D array). Each key represents a piece of information \(i\) to be kept alongside every streamline, and its associated value is a 2D array of shape (\(T\), \(P_i\)) where \(T\) is the number of streamlines and \(P_i\) is the number of values to store for that particular piece of information \(i\).

data_per_pointPerArraySequenceDict object

Dictionary where the items are (str, ArraySequence). Each key represents a piece of information \(i\) to be kept alongside every point of every streamline, and its associated value is an iterable of ndarrays of shape (\(N_t\), \(M_i\)) where \(N_t\) is the number of points for a particular streamline \(t\) and \(M_i\) is the number values to store for that particular piece of information \(i\).

Methods

__init__(streamlines=None, data_per_streamline=None, data_per_point=None, affine_to_rasmm=None)

Parameters

streamlinesiterable of ndarrays or ArraySequence, optional

Sequence of \(T\) streamlines. Each streamline is an ndarray of shape (\(N_t\), 3) where \(N_t\) is the number of points of streamline \(t\).

data_per_streamlinedict of iterable of ndarrays, optional

Dictionary where the items are (str, iterable). Each key represents an information \(i\) to be kept alongside every streamline, and its associated value is an iterable of ndarrays of shape (\(P_i\),) where \(P_i\) is the number of scalar values to store for that particular information \(i\).

data_per_pointdict of iterable of ndarrays, optional

Dictionary where the items are (str, iterable). Each key represents an information \(i\) to be kept alongside every point of every streamline, and its associated value is an iterable of ndarrays of shape (\(N_t\), \(M_i\)) where \(N_t\) is the number of points for a particular streamline \(t\) and \(M_i\) is the number scalar values to store for that particular information \(i\).

affine_to_rasmmndarray of shape (4, 4) or None, optional

Transformation matrix that brings the streamlines contained in this tractogram to RAS+ and mm space where coordinate (0,0,0) refers to the center of the voxel. By default, the streamlines are in an unknown space, i.e. affine_to_rasmm is None.

property affine_to_rasmm

Affine bringing streamlines in this tractogram to RAS+mm.

apply_affine(affine, lazy=False)

Applies an affine transformation on the points of each streamline.

If lazy is not specified, this is performed in-place.

Parameters

affinendarray of shape (4, 4)

Transformation that will be applied to every streamline.

lazy{False, True}, optional

If True, streamlines are not transformed in-place and a LazyTractogram object is returned. Otherwise, streamlines are modified in-place.

Returns

tractogramTractogram or LazyTractogram object

Tractogram where the streamlines have been transformed according to the given affine transformation. If the lazy option is true, it returns a LazyTractogram object, otherwise it returns a reference to this Tractogram object with updated streamlines.

copy()

Returns a copy of this Tractogram object.

property data_per_point

property data_per_streamline

extend(other)

Appends the data of another Tractogram.

Data that will be appended includes the streamlines and the content of both dictionaries data_per_streamline and data_per_point.

Parameters

otherTractogram object

Its data will be appended to the data of this tractogram.

Returns

None

Notes

The entries in both dictionaries self.data_per_streamline and self.data_per_point must match respectively those contained in the other tractogram.

property streamlines

to_world(lazy=False)

Brings the streamlines to world space (i.e. RAS+ and mm).

If lazy is not specified, this is performed in-place.

Parameters

lazy{False, True}, optional

If True, streamlines are not transformed in-place and a LazyTractogram object is returned. Otherwise, streamlines are modified in-place.

Returns

tractogramTractogram or LazyTractogram object

Tractogram where the streamlines have been sent to world space. If the lazy option is true, it returns a LazyTractogram object, otherwise it returns a reference to this Tractogram object with updated streamlines.

Bases: nibabel.nifti1.Nifti1Pair, nibabel.filebasedimages.SerializableImage

Class for single file NIfTI1 format image

Attributes

affine

dataobj

header

in_memory

True when any array data is in memory cache

ndim

shape

slicer

Slicer object that returns cropped and subsampled images

Methods

__init__(dataobj, affine, header=None, extra=None, file_map=None)

Initialize image

The image is a combination of (array-like, affine matrix, header), with optional metadata in extra, and filename / file-like objects contained in the file_map mapping.

Parameters

dataobjobject

Object containg image data. It should be some object that retuns an array from np.asanyarray. It should have a shape attribute or property

affineNone or (4,4) array-like

homogenous affine giving relationship between voxel coordinates and world coordinates. Affine can also be None. In this case, obj.affine also returns None, and the affine as written to disk will depend on the file format.

headerNone or mapping or header instance, optional

metadata for this image format

extraNone or mapping, optional

metadata to associate with image that cannot be stored in the metadata of this image type

file_mapmapping, optional

mapping giving file information for this image format

Notes

If both a header and an affine are specified, and the affine does not match the affine that is in the header, the affine will be used, but the sform_code and qform_code fields in the header will be re-initialised to their default values. This is performed on the basis that, if you are changing the affine, you are likely to be changing the space to which the affine is pointing. The set_sform() and set_qform() methods can be used to update the codes after an image has been created - see those methods, and the manual for more details.

files_types = (('image', '.nii'),)

header_class

alias of Nifti1Header

update_header()

Harmonize header with image data and affine

valid_exts = ('.nii',)